This invention relates to early detection that a person's health status is declining, especially for aged individuals. “Health status” as used herein refers to an individual's present-day level of overall wellness, relative to that same individual's baseline (that is, typical day-to-day level) of wellness. Components of health status may include both physical components, such as physical function or bodily pain, and mental components, such as cognitive function or emotional state.
Early detection of declining health status is especially important in the aged population, driving both societal and economic benefits. On the societal dimension, early detection of declining health status can be an enabler of early intervention, which can in turn lead to more robust health of the affected individuals, greater dignity, and reduced pain and suffering—not just in the aged population, but in the population of supporting caregivers. On the economic dimensions, early detection of declining health status can be an enabler to keeping elderly individuals in their own home and out of institutional care, such as hospitals and nursing homes—which are suppliers of labor-intensive round-the-clock or intensive care, and so are far more expensive than at-home care.
Studies in the geriatric population indicate that mobility problems in the elderly are predictive of near-term disability and injuries (such as falls and fractures). For example, consistent changes in aspects of an individual's gait—such as reduced speed of walking, or reduced step length—are considered to indicate an increased risk of falling. As another example, a generalized reduction in activity level—manifested by, for example, less time each day walking around—may be predictive of early functional deterioration in a disease state (such as worsened congestive heart failure) or of an occult infection (such as pneumonia).
In general, non-transient changes in a person's activity and behavior from baseline may be predictive of imminent deterioration of that person's health status. Accordingly, many schemes have been proposed to detect such changes, and to flag them to caregivers (such as sons and daughters) or to providers (such as nurses and doctors) for possible early intervention.
Personal Emergency Response Systems (PERS) are a widespread, commercially-available class of devices. One example of a PERS device is the Lifeline, manufactured by Philips. PERS devices typically consist of a button that is worn by the individual on a necklace or similar strap; in case of a fall or other emergency event, the individual presses the button to call for help. Some of these devices additionally carry an accelerometer, which can detect when a fall has occurred, and can call for help automatically in case the individual has been incapacitated by the fall and cannot press the button. While very useful, and potentially even life-saving, in the event of an emergency, PERS devices uniformly suffer from one critical deficiency: it is not possible for them to provide any predictive warning before an event, such as a fall, actually occurs. They can only provide early warning after the event has already taken place.
Besides PERS, a number of schemes have been proposed for early detection that an event has occurred. For example, U.S. Pat. No. 7,567,200 to Osterweil describes the use of radar to detect a fall event. As with PERS, all such events cannot provide predictive warning in advance of an event, only warning once the event has already occurred.
A number of schemes for predictive warning of event, such as a fall, involve the wearing of accelerometers or gyroscopes. For example, U.S. Pat. No. 6,895,341 to Barrey describes the use of accelerometers, worn in e.g., a semi-elastic waistband, in order to detect irregularities in an individual's locomotion. Accelerometer-based predictive methods all suffer from one critical drawback, however: they require the individual to physically carry or wear one or more accelerometers, which devices are bulky, cumbersome, uncomfortable, and embarrassing.
Other schemes for predictive warning of an event, such as a fall, rely on detecting vibrations through a floor. For example, U.S. Pat. No. 7,857,771 to Alwan describes the use of floor vibrations to passively detect different types of gait as well as actual falls. Such methods suffer from multiple deficiencies: for example, they cannot readily distinguish between multiple individuals (i.e., cannot assign a particular gait to a particular person), are subject to confounders (such as the presence of visitors, or the local condition of the floor, furniture, or building), and their input data are limited to a single, poorly informative, and noisy signal, thus limiting their accuracy and utility.
Other schemes for predictive warning of an event, such as a fall, rely on establishing an array of motion sensors throughout an area. These motion sensors may or may not require transponders to be worn. For example, U.S. Pat. No. 7,978,085 to Kearns describes the use of trackable transponders, worn by an individual, to monitor activity levels over time.
Other methods establish a network of motion sensors around an individual's home, which are capable of detecting that someone is nearby, and of measuring transition from one part of the home to another. Yet other methods may establish a network of device transducers linked to specific devices in an individual's home, such as toilet or door handles, that send a signal when the linked devices are used. Examples of companies that manufacture or sell either motion or device transducer sensors include Grand Care Systems, Carelnnovations, and BeClose.
For example, U.S. Pat. No. 8,035,526 to Needham describes the use of proximity gradients to detect the presence of an individual and to supply text notification messages to that individual upon such detection. As another example, GPS-based devices are commercially available that can track the location of an individual. All motion-detection or device-transducer methods suffer from multiple deficiencies: for example, they cannot readily distinguish between multiple individuals, they may be vulnerable to interference by local furniture, buildings, or other objects, and their input data are limited to low-information streams that can detect only gross measures of motion or the occurrence of particular events (e.g., toilet flush), not more-precise and informative measures such as gait or posture or generalized activity.
Other schemes for predictive warning of declining health status are disease-specific, and so rely on measuring disease-specific parameters. For example, there are a wide variety of commercially-available devices, such as weight scales, glucometers, peak flow meters, and so on, that measure physiologic or biomarker parameters that are specific to corresponding diseases, such as congestive heart failure, diabetes, or asthma. These schemes are often highly effective in aiding management of the specific disease that they target: however, they are ineffective outside of the province of that disease (for example, no devices appear to yet exist that can provide early warning of pneumonia or dementia), and they are unable to provide early warning of a general deterioration of health status.
Other schemes for predictive warning of declining health status measure aspects of an individual's so-called Activities of Daily Living (ADL's), such as cooking a meal or balancing a checkbook or taking a medication. These schemes incorporate a wide array of devices to monitor specific aspects of ADLs: for example, electronic pillboxes or bottle caps that record whether and when a medication was retrieved by the individual (and, presumably, subsequently taken).
For example, U.S. Pat. No. 7,847,682 to Jung describes the tracking of gross behaviors, such as sleeping, eating, or exercising in order to sense abnormal changes in such behaviors. ADL-based schemes may be effective in tracking adherence to a particular type of desirable behavior, but in general, their information content is too limited to be able to draw conclusions or to provide warnings about overall health status deterioration, and are also subject to confounders.
Other schemes combine aspects of one or more of the aforementioned schemes. For example, US Patent Publication US 2011-0264008 to Yang describes the use of electromyography, accelerometers, and gyroscopes, in order to distinguish an emergency event (such as a fall) from a normal event (such as an ADL). Such combinations fail to overcome the individual deficiencies of each scheme, because the capabilities of all the aforementioned schemes, even considered in aggregate, fail to target or compensate for the root causes of their cumulative deficiencies.
Overall, known methods of early warning of declining health status suffer from one or more of the following disadvantages:                Known methods may require that devices, such as accelerometers or gyroscopes, be worn by an individual;        Known methods may generate data of insufficient quality, precision, or relevancy to provide reliable early warning of declining health status;        Known methods may invade user's privacy, for example, through the use of cameras or video        Known methods may be limited to a specific disease, and therefore ineffective outside the realm of that disease;        Known methods may operate only rarely or intermittently, for example, taking measurements only at certain times of day, or only when the user does a particular activity        Known methods may be active (requiring the user to go out of his or her way to perform some action, such as putting on a sensor, or pressing a button on a machine) rather than passive (where the method runs continually in the background, and doesn't require the user to do anything)        Known methods may be subject to commonly-occurring confounders, such as the presence of more than one person;        Known methods may be expensive and/or complex to execute;        Known methods may require setup and/or ongoing maintenance by dedicated experts in order to function properly;        Known methods may be able to detect an event only after it has occurred, not before; in other words, they may not possess predictive power.        Known methods may not allow real-time, interactive user interfaces, or system responses to user movement or commands.        
Known methods may obtain limited depth knowledge about a scene. “Depth knowledge” or “depth data”, as used herein, refers to gathering information—possibly partial, or incomplete—about the spatial positions of objects in space relative to a known coordinate system. “Image knowledge” or “image data”, as used herein, refers to gathering an image of a scene, which may be in, for example, visual wavelengths or in other wavelengths of the electromagnetic spectrum. “Color image knowledge” or “color image”, as used herein, refers to gathering a visual image of a scene, using color wavelengths, similar to the way in which a standard digital camera gathers a visual image. The term “camera”, as used herein, refers to any sensor that may gather information about the environment, especially (though not limited to) electromagnetic measurements, such as visible or infrared light. “Camera”, as used herein, is thus a general-purpose term, and does not refer specifically to, nor is limited to, visual-light devices.
U.S. Patent Publication 2011-0211044 (Shpunt) teaches a method of gathering depth knowledge about an object through the use of an illumination module, which projects patterned optical radiation onto a scene, and an image capture module, which captures an image of the reflected pattern.
Image and/or depth data may be combined to identify the spatial location of specific human body portions. U.S. Patent Publication 2011-0052006 (Gurman) teaches a method of locating portions of a humanoid form using a temporal sequence of depth maps, where each depth map represents a scene as a two-dimensional matrix of pixels indicating topographic information. U.S. Patent Publication 2011-0211754 (Litvak) teaches a method which processes image and depth data in such a way that specific parts of a body, such as the head, may be identified in the image and depth data. Thus, post-processing of image and/or depth data can generate so-called “skeleton data” or “joint data”, describing the approximate locations in space of specific parts of a person's body.